Analyzing nonequilibrium quantum states through snapshots with artificial neural networks

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Modern applications of machine learning in quantum sciences

A Dawid, J Arnold, B Requena, A Gresch… - ar** out phase diagrams with generative classifiers
J Arnold, F Schäfer, A Edelman, C Bruder - Physical Review Letters, 2024 - APS
One of the central tasks in many-body physics is the determination of phase diagrams.
However, map** out a phase diagram generally requires a great deal of human intuition …
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Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics

A Valenti, G **, J Léonard, SD Huber, E Greplova - Physical Review A, 2022 - APS
Large-scale quantum devices provide insights beyond the reach of classical simulations.
However, for a reliable and verifiable quantum simulation, the building blocks of the …
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Machine learning of implicit combinatorial rules in mechanical metamaterials

R Van Mastrigt, M Dijkstra, M Van Hecke, C Coulais - Physical Review Letters, 2022 - APS
Combinatorial problems arising in puzzles, origami, and (meta) material design have rare
sets of solutions, which define complex and sharply delineated boundaries in configuration …
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Snapshot-based characterization of particle currents and the Hall response in synthetic flux lattices

M Buser, U Schollwöck, F Grusdt - Physical Review A, 2022 - APS
Quantum simulators are attracting great interest because they promise insight into the
behavior of quantum many-body systems that are prohibitive for classical simulations. The …
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